Alluxio is an open source Data orchestration platform that can be deployed on multiple platforms. However, it can require a lot of thinking and experience to integrate Alluxio into an existing Data Architecture adhering to minimally required DevOps principles meeting Organizational standards.
The presentation talks about the best practices to set up and techniques to build a cluster with open source Alluxio on AWS EKS, for one of our clients, which made it Scalable, Reliable, and Secure by adapting to Kubernetes RBAC.
Our speaker Vasista Polali will show you how to :
- Bootstrap EKS cluster in AWS with Terraform.
- Deploy open source Alluxio in a Namespace with persistence in AWS EFS.
- Scale up and down the Alluxio worker nodes as Daemon sets by Scaling the EKS nodes with Terraform.
- Accessing data with S3 mount.
- Controlling the access to Alluxio with Kubernetes port-forwarding, “setfacl” functionality, and Kubernetes service accounts.
- Re-using the data/metadata in the persistence layer on a new cluster.
Alluxio is an open source Data orchestration platform that can be deployed on multiple platforms. However, it can require a lot of thinking and experience to integrate Alluxio into an existing Data Architecture adhering to minimally required DevOps principles meeting Organizational standards.
The presentation talks about the best practices to set up and techniques to build a cluster with open source Alluxio on AWS EKS, for one of our clients, which made it Scalable, Reliable, and Secure by adapting to Kubernetes RBAC.
Our speaker Vasista Polali will show you how to :
- Bootstrap EKS cluster in AWS with Terraform.
- Deploy open source Alluxio in a Namespace with persistence in AWS EFS.
- Scale up and down the Alluxio worker nodes as Daemon sets by Scaling the EKS nodes with Terraform.
- Accessing data with S3 mount.
- Controlling the access to Alluxio with Kubernetes port-forwarding, “setfacl” functionality, and Kubernetes service accounts.
- Re-using the data/metadata in the persistence layer on a new cluster.
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Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
